Keynotes & Special Sessions

To be announced.

Kei Sakaguchi (Senior Member, IEEE) received the M.E. degree in information processing and the Ph.D. degree in electrical and electronics engineering from Tokyo Institute of Technology, Japan, in 1998 and 2006, respectively. Currently, he is the Program Director (VI-ILS), the Dean of the Academy of Super Smart Society, and a Professor with the School of Engineering, Institute of Science Tokyo (formerly Tokyo Institute of Technology). His current research interests include 5G/6G cellular networks, millimeter-wave communications, V2X for automated driving, digital twins, smart mobility, smart agriculture, smart ocean, and smart healthcare. He is a fellow of IEICE. He received the Outstanding Paper Awards from SDR Forum and IEICE, in 2004 and 2005, respectively, and the three Best Paper Awards from the IEICE Communication Society, in 2012, 2013, and 2015. He also received the Tutorial Paper Award from the IEICE Communication Society, in 2006.

Due to the explosive growth in the number of wireless devices and diverse wireless services, next-generation wireless networks face unprecedented challenges caused by heterogeneous data traffic, massive connectivity, ultra-high bandwidth efficiency and ultra-low latency requirements. To address these challenges, flexible-antenna systems have been recognized as key enabling technologies of the sixth-generation (6G) wireless networks, as they can intelligently reconfigure users’ effective channel gains and hence significantly enhance their data transmission capabilities. However, the existing flexible-antenna systems have been developed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they are lack of the capability to reconstruct strong line-of-sight (LoS) links which are typically 100 times stronger than NLoS links. Furthermore, the existing flexible-antenna systems exhibit restricted flexibility, where adding/removing an antenna is not straightforward. This talk focuses on an innovative flexible-antenna system, termed generalised pinching-antenna systems, and how such a new physical technology can reshape the future network architecture. The principles of generalized pinching-antenna systems are described first together with specific examples of generalized pinching-antenna systems, including Docomo’s dielectric waveguide based pinching antennas, leaky coaxial cable (LCX), etc. In addition, promising 6G related applications of generalized pinching antennas, including environment division multiple access (EDMA), integrated sensing and communication, multi-cell interference management, etc, are also illustrated. Finally, important directions for future research, such as antenna/waveguide deployment, channel estimation, etc, are highlighted.

Zhiguo Ding is currently a Professor in Communications at Nanyang Technological University and an Academic Visitor at Princeton University. His research interests are 6G networks, communications and signal processing. His h-index is over 130 and his work receives 80,000+ Google citations. He is serving as the EiC of IEEE JSAC, an Area Editor for the IEEE TWC and OJSP, an Editor for IEEE TVT, and OJ-SP, and was an Area Editor for IEEE TCOM and OJCOMS, an Editor for IEEE TCOM, TWC, COMST, WCL, CL and WCMC. He received the best paper award of IET ICWMC-2009 and IEEE WCSP-2014, the EU Marie Curie Fellowship 2012-2014, the Top IEEE TVT Editor 2017, IEEE Heinrich Hertz Award 2018, IEEE Jack Neubauer Memorial Award 2018, IEEE Best Signal Processing Letter Award 2018, Alexander von Humboldt Foundation Friedrich Wilhelm Bessel Research Award 2020, IEEE SPCC Technical Recognition Award 2021, IEEE VTS Best Magazine Paper Award 2023, and the Best Paper Award in IEEE GLOBCOM 2024. He is a Web of Science Highly Cited Researcher in two disciplines (2019-2025), and a Fellow of the IEEE.

To be announced.

To be announced.

Device-free localization techniques using channel state information (CSI) in wireless local area networks (WLANs) have been extensively studied. In these techniques, the collected CSI is used as a dataset for machine-learning (ML)-based localization. This talk presents experimental results and comparisons of a recently developed WLAN-based device-free localization scheme under different environmental scenarios. The results demonstrate that the effectiveness of the localization scheme depends on the placement of WLAN transceivers and the presence of reflective obstacles near the experimental area.

Osamu Muta received the Associate Degree in Engineering from Sasebo Institute of Technology in 1994, the B.E. degree from Ehime University in 1996, the M.E. degree from Kyushu Institute of Technology in 1998, and the Ph.D. degree from Kyushu University in 2001. In 2001, he joined the Graduate School of Information Science and Electrical Engineering, Kyushu University, as an Assistant Professor. From 2010 to 2023, he was an Associate Professor at the Center for Japan-Egypt Cooperation in Science and Technology, Kyushu University. Since 2023, he has been a Professor at the Faculty of Information Science and Electrical Engineering, Kyushu University, Japan. His research interests include signal processing for wireless and power line communications, MIMO techniques, interference coordination, wireless sensing, low-power wide-area networks, and nonlinear distortion compensation for high-power amplifiers.

In this talk, I introduce the potential of Deep Joint Source-Channel Coding (DeepJSCC) for underwater networks. DeepJSCC directly transmits latent image representations as IQ signals and is well suited to challenging underwater channels. We demonstrated its effectiveness in underwater acoustic, underwater optical, and 5G systems, and achieved real-time FPGA operation at approximately 33 fps with low power consumption.

Daisuke Hisano received the B.E., M.E., and Ph.D. degrees from Osaka University, Osaka, Japan, in 2012, 2014, and 2018, respectively. Since April 2026, he has been an Associate Professor at Keio University. His research interests include AI-driven communications, optical communications, and all-optical signal processing.

Stacked intelligent metasurfaces (SIMs) have recently emerged as a promising technology for manipulating electromagnetic waves directly in the wave domain. By stacking multiple programmable metasurface layers, SIMs can provide richer spatial transformations than conventional single-layer intelligent surfaces, opening new possibilities for the design of future wireless communication systems. This talk provides an overview of SIMs from the perspectives of both electromagnetic wave propagation and wireless communications. We first introduce the basic concept and operating principles of SIMs, with particular attention to how electromagnetic waves propagate through and between multiple metasurface layers. We then discuss several approaches to modeling such propagation and highlight the importance of appropriately capturing the physical characteristics of stacked structures. While simplified propagation models are useful for communication-theoretic analysis and optimization, practical SIM implementations may involve various electromagnetic effects, such as propagation loss, coupling, and multiple interactions among metasurface layers. These effects can influence the effective signal transformation realized by SIMs and consequently their achievable communication performance. The talk will also introduce recent research directions and selected examples of SIM-assisted wireless communication systems, including wave-domain signal processing and advanced multiple-access techniques. Through these examples, we discuss how the physical characteristics of SIMs can be incorporated into communication-system design and optimization. Finally, we briefly present emerging applications and open research challenges toward practical SIM-enabled wireless systems.

Hiroaki Hashida is an assistant professor with the Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai, Japan. He received his Ph.D. degree in information science from Tohoku University in 2024. He was a recipient of the Presidential Award for Outstanding Students from Tohoku University and the Ikushi Prize from the Japan Society for the Promotion of Science (JSPS) in 2024. His research interests include wireless communication networks, intelligent surface-aided wireless communication systems, and stacked intelligent metasurfaces. His research focuses on the design and optimization of future wireless communication systems by integrating advanced wireless technologies with programmable and reconfigurable radio environments. In particular, he is interested in developing efficient methods for controlling and utilizing intelligent surfaces to improve the coverage, reliability, and efficiency of wireless networks. He is a member of the IEEE and the Institute of Electronics, Information and Communication Engineers.

In this talk, we will introduce two innovative types of 3D intelligent metasurfaces: Stacked Intelligent Metasurfaces (SIM) and Flexible Intelligent Metasurfaces (FIM). We will explore their exciting applications in wireless communication and sensing systems. Specifically, SIM is a groundbreaking computing architecture that enables joint signal processing and communication in the electromagnetic (EM) domain. A SIM is fabricated by stacking an array of programmable metasurface layers, where each layer consists of many low-cost passive meta-atoms that can individually manipulate EM waves. By appropriately configuring the passive meta-atoms, a SIM can automatically accomplish advanced computation tasks as the EM wave propagates through it while reducing both energy consumption and processing delay. By contrast, an FIM takes a different approach to leverage the 3D physical space. An FIM is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals while flexibly adjusting its position along the direction perpendicular to the surface. Hence, unlike conventional rigid 2D antenna arrays, the FIM surface shape may be dynamically reconfigured to improve the channel quality by beneficial 3D morphing.

Chau Yuen received the B.Eng. and Ph.D. degrees from Nanyang Technological University, Singapore, in 2000 and 2004, respectively. Since 2023, he has been with the School of Electrical and Electronic Engineering, Nanyang Technological University. Dr. Yuen received IEEE Communications Society Leonard G. Abraham Prize (2024), IEEE Communications Society Best Tutorial Paper Award (2024), IEEE Communications Society Fred W. Ellersick Prize (2023), IEEE Marconi Prize Paper Award in Wireless Communications (2021), IEEE APB Outstanding Paper Award (2023), and EURASIP Best Paper Award for JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING (2021). He is an IEEE Fellow and also a Highly Cited Researcher by Clarivate Web of Science.

Detection of illegal and unwanted UAVs is in high demand for air safety and security in civil society. Conventional optical imaging offers cost-effective and attractive solutions; however, adverse weather conditions such as fog and darkness prevent successful detection. Millimeter-wave radar systems provide another possible solution, but certain UAV shapes and orientations can dramatically degrade the reflected signal strength. We are currently developing a multistatic imaging system combining synchronized millimeter-wave radars with multilateration-based detection, leveraging both communication and radar sensing techniques. In this talk, we will briefly introduce the overview and principles of the system, with a particular focus on wireless clock and frequency synchronization techniques.

Mr. Kunihiko Sasaki is a visiting professor at the Nagoya Institute of Technology. For over 40 years, he has conducted research on technologies such as wireless communication, antenna propagation, and wireless power transfer at DENSO Corporation's research laboratories. Concurrently, he has spearheaded standardization efforts related to the Japanese government's Radio Act.

The growing demands of satellite sensing and non-terrestrial networks call for efficient onboard processing, low-latency inference, and reduced dependence on resource-intensive digital computation. Conventional satellite remote-sensing systems typically follow a “digitize-then-process” paradigm, where high-dimensional sensing data are digitally processed onboard or transmitted to terrestrial stations for subsequent reconstruction and semantic inference, resulting in substantial computational and communication overhead.

This talk explores an alternative “compute-while-transmitting” paradigm that exploits electromagnetic wave propagation as a physical computational process. As a representative case study, we present a stacked intelligent metasurface-based diffractive neural network for onboard terrain classification directly from Synthetic Aperture Radar (SAR) Level-0 raw data. Multiple programmable metasurface layers are jointly optimized to perform task-oriented feature mapping in the wave domain, while received signal intensities at the terrestrial station directly provide semantic classification outputs. A lightweight phase-domain augmentation strategy is further introduced to improve the learnability of complex-valued raw SAR signals under speckle, Doppler distortions, and other impairments.

Numerical results demonstrate approximately 90% classification accuracy for binary terrain recognition directly from real SAR Level-0 data, while reducing reliance on conventional digital processing and high-dimensional raw-data transmission. More complex multi-class experiments further reveal the representational limitations of purely linear in-wave processing, motivating nonlinear and hybrid physical computing mechanisms.

Beyond SAR classification, this work highlights the potential of programmable electromagnetic structures to evolve from communication components into task-oriented physical computing platforms, opening opportunities for integrated sensing, communication, and computation in future terrestrial and non-terrestrial network architectures.

Mengbing Liu received the B.E. degree in Electronic Information Engineering from Northeastern University, China, in 2018, and the M.E. degree in Electronics and Communications Engineering from the University of Science and Technology of China, China, in 2021. She is currently pursuing the Ph.D. degree with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.

Her research focuses on physics-driven machine learning and intelligent wireless systems, with particular interests in stacked intelligent metasurfaces, reconfigurable intelligent surfaces, in-wave and analog computing, and integrated communication, sensing, and computation. Her recent work explores programmable electromagnetic structures for physical-layer inference and task-oriented processing in next-generation wireless and satellite systems. Her research has appeared in leading venues including IEEE TWC, IEEE TCCN, and IEEE Wireless Communications, among others.

To be announced.

To be announced.